In the domain of clinical AI, it is essential to understand the pertinent reasons for incorporating AI, rather than solely focusing on the potential benefits. The choice to integrate AI should not be a whimsical decision, but one informed by concrete objectives to address specific issues.
Three distinct strategies to consider while integrating AI in…
In the high-speed field of stroke care, artificial intelligence (AI) can significantly impact patient results, with Aidoc’s Stroke Care Coordination (CareCo) App being a prime example. The app has transformed stroke care delivery across multiple partner facilities through three key characteristics of its successful implementation - dynamic project champions, establishing clear success metrics, and comprehensive…
The author, a radiologist for one of the top Artificial Intelligence (AI) companies, Aidoc, discusses the challenges of implementing AI algorithms in radiology departments. The author uses the analogy of their past experiences repairing motorcycles to explain how deploying AI in healthcare settings often involves a collage of makeshift solutions reminiscent of duct tape, rather…
The following article details the author's experience of working at Aidoc, a leading medical AI company, despite lacking a detailed understanding of software engineering, data security, and AI, drawing parallels between his experience repairing old motorcycles and developing and deploying AI algorithms in medical settings.
The author introduces the topic by confessing his lack of comprehensive…
The importance of artificial intelligence (AI) in clinical settings has grown significantly, sparking discussions on best practice for AI governance structures and identifying key stakeholders in building a long-lasting AI strategy. Specifically, radiologists have been at the forefront of embracing AI, acknowledging the potential it holds to transform their workflows, such as reducing reading times…
The changing nature of the regulatory environment for clinical AI presents unique challenges for leaders in the healthcare sector. As healthcare systems look to adopt and implement AI responsibly, a range of regulatory compliance issues have come to the fore, bringing with them the need for effective internal governance structures. The application of AI in…
The Modern Healthcare Digital Health Summit recently highlighted the value of a strategic approach to maximizing AI's impact in the healthcare industry, improving cross-departmental efficiency, and enabling preventative care. Aidoc CEO Elad Walach shared his views on the evolving landscape of clinical AI in a conversation with journalist Gabriel Perna.
Walach pointed out the setbacks of…
Data drift is a phenomena that impacts any AI model in current operation. It is essentially a change in the features distribution an AI model receives while it's in production, thereby leading to a decline in the model's performance. A visible impact in imaging AI, for instance, could be an algorithm becoming less reliable at…